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研究生:林佳儀
研究生(外文):Chia-Yi Lin
論文名稱:以有效的模糊C-平均值演算法分析馬達電流訊號與辨識馬達的品質類別
論文名稱(外文):A Fuzzy C-Means Algorithm to Analyze Current Waveform for Determining the Motor’s Quality Types
指導教授:廖炯州
指導教授(外文):Chiung-Chou Liao
學位類別:碩士
校院名稱:健行科技大學
系所名稱:電子工程系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:106
語文別:中文
論文頁數:27
中文關鍵詞:模糊C-平均值演算法(Fuzzy C-MeansFCM)集群分析(Cluster Analysis)歐氏距離(Euclidean Distance)直流馬達(DC Motor)
外文關鍵詞:Fuzzy C-Means (FCM)Cluster AnalysisEuclidean DistanceDC Motor
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本篇論文提出「以有效的模糊C-平均值演算法(Fuzzy C-Means, FCM)分析馬達電流訊號與辨識馬達的品質類別」。本論文是將FCM演算法應用於辨識馬達的品質類別為例說明。辨識過程簡述如下:(i)首先,從已知品質類別的樣本馬達中,取得各種品質類別的輸入訊號,並分別計算在各種不同品質類別之主要特徵點的特徵值向量;(ii)其次,執行FCM演算法,並計算在所有不同品質類別中,每一個品質類別之最終的群體中心值;(iii)接著,計算下列兩者之間的歐氏距離,它們分別是:待辨識馬達之主要特徵點的特徵值向量,及每一種品質類別之最終的群體中心值;(iv)最後,依據計算得到的歐氏距離,決定該待辨識馬達之品質類別是屬於何種類別。若是屬於“品質類別-z”,條件是在所有的品質類別中,該待辨識馬達的主要特徵值向量,它與“品質類別-z”之最終的群體中心值,兩者之間的歐氏距離是最小的值。本篇論文經過多次的測試,證實以模糊C-平均值演算法應用於分析電流訊號與辨識馬達的品質類別,是一個有效且實用的方法。
This dissertation proposes a novel Fuzzy C-Means (FCM) algorithm for determining the motors’ quality types by analyzing their current waveforms. The determining process of motors’ quality types are listed as follows: (i) Obtaining input current signals of sample motors with well-known distinct classes and then calculating their feature values of qualitative features; (ii) Computing centroid values of each quality class; (iii) Computing Euclidean distances of their feature values and centroid values of classes; and (iv) Determining motors’ quality types using obtained Euclidean distances. A motor belongs to a class-z if it and class-z has the minimum Euclidean distance. Experimental results show that the proposed FSM algorithm is an effective quality determining method for motors.
摘  要......................................................................i
Abstract......................................................................ii
誌  謝......................................................................iii
表目錄..........................................................................v
圖目錄..........................................................................vi
第一章 前言..................................................................1
第二章 模糊C-平均值分群演算法(FCM)的回顧...............4
第三章 FCM演算法的應用:辨識直流馬達的品質類別.....6
3.1 主要特徵點的定義....................................................7
3.2計算樣本馬達之各主要特徵點在各種馬達品質類別的特徵值範圍 ....................................................................................9
3.3計算樣本馬達之各主要特徵點在各種馬達品質類別的最終群體中心值................................................................................12
3.4 決定待測試馬達之最終品質類別..............................14
第四章 性能評估...........................................................18
4.1實驗一:辨識單一週期之馬達電流訊號的品質類別.....18
4.2實驗二:辨識單一顆馬達的品質類別........................20
4.3實驗三:正確辨識率................................................21
第五章 結論..................................................................23
參考文獻......................................................................24
簡 歷.........................................................................27
[1] 王文俊, “認識Fuzzy,” 全華科技圖書公司, 2007.
[2] J. C. Dunn, “A fuzzy relative of the ISODATA process and its use in detecting compact Well-Separated clusters,” Journal of Cybernetics, vol. 3, pp 32-57, 1973.
[3] J. C. Bezdek, “Pattern recognition with fuzzy objective function algorithms,” New York, Plenum Press, 1981.
[4] Y. C. Yeh, W. J. Wang, and C. W. Chiou, “A novel fuzzy c-means method for classifying heartbeat cases from ECG signals,” Measurement, vol. 43, no. 10, pp. 1542-1555, 2010.
[5] W. C. Chen, and M.S. Wang, “A fuzzy c-means clustering-based fragile watermarking scheme for image authentication,” Expert System with Application vol. 36, pp. 1300-1307, 2009
[6] S. H. Lin, K. M. Chang, and C. C. Tyan, “Fuzzy c-means clustering for myocardial ischemia estimation with pulse waveform analysis,” Biomedical Engineering: Applications, Basis and Communications, vol. 21, pp. 139-147, 2009.
[7] L. C. Lin, Y. C. Yeh, and Y. S. Lin, “A Mahalanobis Distance Measurement (MDM) method to analyze current waveform for determining the motor’s quality types”, The 12th International Symposiumon Measurement Technology and Intelligent Instruments (ISMTII 2015), Taipei, Taiwan, September 22~25, 2015.
[8] Y. C. Yeh, L. C. Lin, M. C. Liu, and T. S. Chu, “Feature selection algorithm for motor quality types using Weighted Principal Component Analysis”, Lecture Notes in Electrical Engineering, vol. 345, pp. 151-157, 2016.
[9] A. Kapun, M. Curkovic, A. Hace, and K. Jezernik, “Identifying dynamic model parameters of a BLDC motor”, Simulation Modelling Practice and Theory, vol. 16, pp. 1254-1265, 2008.
[10] 劉之松, 葉雲奇, “有效的模糊C-平均值演算法及其應用於集群的分析”, 健行科技大學碩士論文, 2016.
[11] 郭建得, 葉雲奇, 陳麗如, ”以FCM演算法辨識心電圖的心跳類別,” 健行科技大學電子工程研究所碩士論文, 2015.
[12] 葉雲奇, 林綠綺, 林佳儀, 陳俊瑋,”有效的分群技術:模糊C-平均值演算法”, 2016民生電子研討會,花蓮,台灣, November 19, 2016.
[13] L. C. Lin, Y. C. Yeh, and Y. W. Song, “Effective Motor’s Quality Types Determination on Motor’s Current Waveforms Using the Euclidean Distance Measurement Method”, 2014 International Symposium on Computer, Consumer and Control, Taichung, Taiwan, Pages 1225-1228, June 10-12, 2014.
[14] L. C. Lin, and Y. C. Yeh, “Determining motor’s quality types using principal component analysis on current waveforms”, Journal of Chien-Hsin University, vol. 34, no. 3, pp. 1-16, 2014.
[15] Y. C. Yeh, “Fuzzy logic method for motor quality types on current waveforms”, Measurement, vol. 46, no. 5, pp. 1682-1691, 2013.
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